Machine learning of network inference enhancement from noisy measurements

TL;DR

This paper introduces a versatile framework that significantly improves network inference accuracy from noisy time series data across various complex systems, enhancing practical applicability.

Contribution

The authors propose a novel, model-agnostic framework that boosts network inference performance in noisy real-world scenarios, applicable to both model-based and model-free methods.

Findings

Substantial performance improvements in noisy conditions

Effective across nonlinear dynamics, games, and epidemic models

Enhanced results with cleaner data samples

Abstract

Inferring networks from observed time series data presents a clear glimpse into the interconnections among nodes. Network inference models, when dealing with real-world open cases, especially in the presence of observational noise, experience a sharp decline in performance, significantly undermining their practical applicability. We find that in real-world scenarios, noisy samples cause parameter updates in network inference models to deviate from the correct direction, leading to a degradation in performance. Here, we present an elegant and efficient model-agnostic framework tailored to amplify the capabilities of model-based and model-free network inference models for real-world cases. Extensive experiments across nonlinear dynamics, evolutionary games, and epidemic spreading, showcases substantial performance augmentation under varied noise types, particularly thriving in scenarios enriched with clean samples.